基于卷积神经网络与Transformer的电能质量扰动分类方法  

Power Quality Disturbances Classification Method Based on Convolutional Neural Network and Transformer

作　　者：金星[1] 周凯翔 于海洲 王盛慧[1] 伍孟海 JIN Xing;ZHOU Kai-xiang;YU Hai-zhou;WANG Sheng-hui;WU Meng-hai(School of Electrical and Electronic Engineering,Changchun University of Technology,Changchun 130000,China)

机构地区：[1]长春工业大学电气与电子工程学院,长春130000

出　　处：《科学技术与工程》2024年第16期6726-6733,共8页Science Technology and Engineering

基　　金：吉林省科技发展计划项(20230203195SF)。

摘　　要：复杂电能质量扰动(power quality disturbances, PQD)的智能分类对于智能电网发展具有重要意义。扰动特征的提取与定位、模式识别与分类是电能质量扰动分类方法研究的难点。采用深度学习算法,将具有关注全局信息的Transformer与善于提取局部特征的卷积神经网络相融合,提出一种基于卷积神经网络(convolutional neural network, CNN)与Transformer的电能质量扰动分类方法,即CTranCBA。这种双深度学习模型分类方法主要是通过一维卷积神经网络提取电能质量扰动信号特征,利用Transformer自注意力机制引导模型关注序列中不同位置间的依赖关系,实现对扰动信号局部特征与全局特征的互补,克服了因感受野的限制而带来的识别不清、分类不准等问题。使用23种不同电能质量扰动信号,将CTranCBA与Deep-CNN、CNN-LSTM、CNN-CBAM方法进行比较。结果表明:该方法在分类准确率和抗噪性方面表现优异,可为电能质量扰动智能分类提供一种新的方法。It is of great significance to the development of smart grid on intelligent classification of complex power quality disturbances(PQD).Disturbance feature extraction and location,pattern recognition and classification are the difficulties of power quality disturbance classification.Based on deep learning,Transformer,which paid attention to global information,was combined with convolutional neural network(CNN).It was good at extracting local features.A power quality disturbance classification method based on CNN and Transformer,namely CTranCBA,was proposed.This dual-deep learning model classification mainly extracted the characteristics of power quality disturbance signals by one-dimensional CNN,and used Transformer self-attention mechanism to guide the model to pay attention to the dependence between different positions in the sequence.As a result,it realized the complementarity of local characteristics and global characteristics of disturbance signals,and overcame the problems of unclear recognition and inaccurate classification caused by the limitation of receptive fields.The CTranCBA was compared with Deep-CNN,CNN-LSTM,CNN-CBAM in 23 different power quality disturbance signals.The results show that the CTranCBA is superior in classification accuracy and noise immunity,which can provide a new method for intelligent classification of power quality disturbance.

关 键 词：电能质量扰动(PQD) 卷积神经网络(CNN) Transformer模型 卷积注意力机制 

分 类 号：TM711[电气工程—电力系统及自动化]